Statistics and Data Science Seminar
Jian Zou
Worcester Polytechnic Institute
Pattern Detection for High-Frequency Financial Time Series via Clustering and Bi-Clustering
Abstract: Exploring high frequency transaction level financial data is of considerable interest to researchers
and investors. The extra amount of information contained in high-frequency data and keen
interests in high-frequency finance motivate researchers to study dynamic patterns of comovement over multiple trading days. In this paper, we have developed a series of clustering and
biclustering algorithms based on mutual information for high frequency financial time series. We
examine the co-movement probabilities of selected m-tuples of stocks over multiple trading days
under different metrics. Additionally, we propose a unified framework to describe patterns and
monitor the structure of high-dimensional daily or weekly time series that track linkages between
any given m-tuple of stocks over a long time period.
Wednesday February 10, 2021 at 4:00 PM in Zoom